1. Impaired striatal-frontal circuits in cocaine addiction predict compulsive and impulsive behaviors in cocaine users In this project, we used resting-state functional connectivity (rsFC) to investigate striatal networks in cocaine users. Increased rsFC strength was observed predominantly in striatal-frontal circuits while decreased rsFC was found in striatal-cingulate, -striatum, -temporal, -hippocampus and -insula circuits in the cocainegroup compared with controls. Increasedstriatal-dorsal lateral prefrontal cortex connectivity strength was positively correlated with recent cocaine use and elevated impulsivity in users, while the balance between striatal-dorsal anterior cingulate cortex and striatal-inferior prefrontal cortex circuits was significantly associated with cocaine compulsive behaviors. (JAMA Psychiatry. 72(6):584-92, 2015) 2. Interactions between the salience and default-mode networks are disrupted in cocaine addiction. Cocaine dependence is a complex neuropsychiatric disorder manifested as dysregulation of multiple behavioral, emotional and cognitive constructs. Neuroimaging studies have begun to identify specific neurobiological circuit impairments in cocaine dependent (CD) individuals that may underlie these symptoms. However, whether, where and how the interactions within and between these circuits are disrupted remains largely unknown. We employed resting-state functional MRI and modularity network analysis to identify brain modules of a priori interest (default-mode network (DMN), salience network (SN), executive control network (ECN), medial temporal lobe (MTL) and striatum) in 47 CD and 47 matched healthy control (HC) participants, and explored alterations within and between these brain modules as a function of addiction. At the module level, inter-module connectivity decreased between DMN and SN in CD. At the nodal level, several regions showed decreased connections with multiple modules in CD: the rostral anterior cingulate connection strength was reduced with SN and MTL; the posterior cingulate had reduced connections with ECN; and the bilateral insula demonstrated decreased connections with DMN. Furthermore, alexithymia, a personality trait previously associated with addiction, correlated negatively with intra-module connectivity within SN only in cocaine users. Our results indicate that cocaine addiction is associated with disrupted interactions among DMN, MTL and SN, which have been implicated respectively, in self-referential functions, emotion and memory, and coordinating between internal and external stimuli, providing novel and important insights into the neurobiological mechanisms of cocaine addiction (Journal of Neuroscience 35: 8081-8090, 2015). 3. Baseline hippocampal activity and its resting-state functional connectivity (rsFC) predict cocaine relapse Neuroplastic changes induced by cocaine use may underlie the heightened relapse rate. Using regional cerebral blood flow (rCBF) as a basal neuronal activity index, this study evaluated alterations in rCBF and the related rsFC to predict relapse in patients following treatment for cocaine use disorder. We found that cocaine-dependent (CD) participants who relapsed within 30 days after treatment discharge showed enhanced rCBF in the left posterior hippocampus (pHp) compared with control groups and the CD users who did not relapse early (early remission subjects). Compared to the early remission participants, the CD participants also demonstrated increased rsFC strength between the posterior cingulate cortex / precuneus and the identified pHp region in the rCBF analysis. These increased basal activities may reflect the heightened reactivity to cocaine cues and persistent cocaine-related ruminations. Together, increased pHp rCBF and strengthened pHp-PCC/PCu rsFC strength predicted relapse to cocaine use up to six months following treatment. Finding mechanisms to suppress hyperactivated brain regions and normalize dysregulated neural circuits may prove useful to prevent relapse in patients with cocaine use disorder. (Biol Psychiatry, in press). 4. Topologically Reorganized Connectivity Architecture of Default-Mode, Executive-Control and Salience Networks across Working Memory Task Loads. The human brain is intrinsically organized into a set of spatially-distributed, functionally-specific networks. Of the major brain networks, the default mode network (DMN), executive control network (ECN) and salience network (SN) have received the most attention recently for their potential roles in cognitive functions. However, it is largely unclear whether, how, and where the interactions within and between these three networks would be modulated by cognitive tasks. Here, we employed graph-based modularity analysis to identify the DMN, ECN and SN during an N-back working memory (WM) task and systematically investigated the modulation of intra- and inter-network interactions at different cognitive loads. As the cognitive load elevated, functional connectivity decreased within the DMN while increased within the ECN; and the SN connected more with both the DMN and ECN. Within-network connectivity of the ventral and dorsal posterior cingulate cortex was differentially modulated by cognitive load. Further, the superior parietal regions within the ECN that showed more inter-network connections at higher WM loads correlated positively with WM task performance. Together, these findings advance our understanding of dynamic integrations of specialized brain systems during normal cognitive functioning and may serve as a baseline for assessing potential disruptions of these interactions in pathological conditions (Cerebral Cortex, in press). 5. Detecting static and dynamic differences between eyes-closed and eyes-open resting states using ASL and BOLD fMRI Resting-state fMRI studies have increasingly focused on multi-contrast techniques, such as BOLD and ASL imaging. However, these techniques may reveal different aspects of brain activity (e.g., static vs. dynamic), and little is known about the similarity or disparity of these techniques in detecting resting-state brain activity. It is therefore important to assess the static and dynamic characteristics of these fMRI techniques to guide future applications. Here we acquired fMRI data while subjects were in eyes-closed (EC) and eyes-open (EO) states, using both ASL and BOLD techniques, at two research centers (NIDA and HNU). Static brain activity was calculated as voxel-wise mean cerebral blood flow (CBF) using ASL, i.e., CBF-mean, while dynamic activity was measured by the amplitude of low frequency fluctuations (ALFF) of BOLD, i.e., BOLD-ALFF, at both NIDA and HNU, and CBF, i.e., CBF-ALFF, at NIDA. We showed that mean CBF was lower under EC than EO in the primary visual cortex, while BOLD-ALFF was higher under EC in the primary somatosensory cortices extending to the primary auditory cortices and lower in the lateral occipital area. Interestingly, mean CBF and BOLD-ALFF results overlapped at the visual cortex to a very small degree. Importantly, these findings were largely replicated by the HNU dataset. State differences found by CBF-ALFF were located in the primary auditory cortices, which were generally a subset of BOLD-ALFF and showed no spatial overlap with CBF-mean. In conclusion, static brain activity measured by mean CBF and dynamic brain activity measured by BOLD- and CBF-ALFF may reflect different aspects of resting-state brain activity and a combination of ASL and BOLD may provide complementary information on the biophysical and physiological processes of the brain. (PLoS One. 2015 Mar 27;10(3):e0121757.)